Article directory Undertake In the previous article, we used original pytorch to implement multi-GPU training and mixed precision. Now comparing the above code, we use Fabric to achieve the same function. Regarding Fabric, I will continue to explain it in subsequent blogs. It is explaining and learning. Through fabric, you can reduce the amount of […]
Tag: multi-gpu
Two multi-GPU distributed training methods using DataParallel and DistributedDataParallel include transfer learning of loading single-GPU and multi-GPU saved pre-trained model weights.
The first use of DataParallel distributed training This is the simplest way to use it. It only requires a few lines of code to use. However, it is relatively inefficient. The principle of this distributed use is to first record all the data to the main card, such as the GPU numbered 0, and then […]
Pytorch implements multi-GPU data parallel model acceleration training based on ray and accelerate
Based on the use of pytorch’s DDP native code, the two libraries ray and accelerate have made a more friendly package for the use of pytorch parallel training code. The following is a minimalist code example. ray ray.py #coding=utf-8 import os importsys import time import numpy as np import torch from torch import nn import […]
[Distributed training] Multi-GPU distributed model training based on PyTorch (supplement)
Multi-GPU distributed model training based on PyTorch (supplement) background knowledge How Data Parallel Works Data distribution process 1. Process initialization 2. Process synchronization data parallelism Benchmarks Relevant information Introduction: Using DistributedDataParallel in PyTorch for multi-GPU distributed model training. Original link: https://towardsdatascience.com/distributed-model-training-in-pytorch-using-distributeddataparallel-d3d3864dc2a7 With the continuous emergence of large models represented by ChatGPT, how to train large […]
Multi-GPU training network model – data parallel (DataParallel, DistributedDataParallel)
Directory I. Overview 1. Distributed and parallel 2. Model parallelism and data parallelism: 3. Data-based parallelism 4.DP (DataParallel) and DDP (DistributedDataParallel) 2. Realization of DP 3. Realization of DDP Fourth, the advantages and disadvantages of DP and DDP 1. Advantages and disadvantages of DP 2. Advantages and disadvantages of DDP 5. The difference between using […]
Pytorch implements multi-GPU parallel training (DDP)
Pytorch usually has two interfaces for parallel training: DP (DataParallel) and DDP (DistributedDataParallel)< /strong>. At present, DP (DataParallel) has been officially deprecate by Pytorch for two reasons: 1. DP (DataParallel) only supports multiple cards for a single machine, but cannot support multiple cards for multiple machines; 2, DP (DataParallel)even if In the single-machine multi-card mode, […]
Pytorch realizes multi-GPU data parallel model acceleration training based on DP and DDP
In the process of deep learning AI model training, you can make full use of multiple GPUs to accelerate parallel training of the model. Usually parallel training is divided into two types: Model parallelism: the model structure is too large, split to multiple GPUs Data parallelism: the training data is too large to be split […]
A simple multi-GPU server monitoring program
Foreword Because there are many GPU servers in the laboratory, every time you want to run the code, you have to run up one by one to see if the GPU is used by anyone, so I wrote a small program like this, the code address is here Effect image curl http://127.0.0.1:7070/info >> 2023-06-03 12:01:31 […]
pytorch model training fp16, apm, multi-GPU model, gradient checkpoint (gradient checkpointing) memory optimization, etc.
The content of this chapter is divided into four parts, fp16, apm and pytorch multi-gpu training mode, gradient checkpointing memory optimization. This section is based on pytorch==1.2.0, transformers==3.0.2 python==3.6 The version of pytorch 1.6 + has its own amp mode, which will not be discussed here for the time being, and will be supplemented later. […]
Multi-GPU parallel training based on tensorflow2.x
Due to the recent training of the transformer, the video memory on one card is not enough, and the other card cannot be loaded, so try to use a dual-card parallel strategy. The basic process and problems encountered are summarized here. Explanation of distribution strategy Use the official tf.distribute.MirroredStrategy as the distribution strategy. This strategy […]